María Asunción Romero Díaz

A New Algorithm for the On-Board Compression of Hyperspectral Images

Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

A new comparison of hyperspectral anomaly detection algorithms for real-time applications

Due to the high spectral resolution that remotely sensed hyperspectral images provide, there has been an increasing interest in anomaly detection. The aim of anomaly detection is to stand over pixels whose spectral signature differs significantly from the background spectra. Basically, anomaly detectors mark pixels with a certain score, considering as anomalies those whose scores are higher than a threshold. Receiver Operating Characteristic (ROC) curves have been widely used as an assessment measure in order to compare the performance of different algorithms. ROC curves are graphical plots which illustrate the trade- off between false positive and true positive rates. However, they are limited in order to make deep comparisons due to the fact that they discard relevant factors required in real-time applications such as run times, costs of misclassification and the competence to mark anomalies with high scores. This last fact is fundamental in anomaly detection in order to distinguish them easily from the background without any posterior processing. An extensive set of simulations have been made using different anomaly detection algorithms, comparing their performances and efficiencies using several extra metrics in order to complement ROC curves analysis. Results support our proposal and demonstrate that ROC curves do not provide a good visualization of detection performances for themselves. Moreover, a figure of merit has been proposed in this paper which encompasses in a single global metric all the measures yielded for the proposed additional metrics. Therefore, this figure, named Detection Efficiency (DE), takes into account several crucial types of performance assessment that ROC curves do not consider. Results demonstrate that algorithms with the best detection performances according to ROC curves do not have the highest DE values. Consequently, the recommendation of using extra measures to properly evaluate performances have been supported and justified by the conclusions drawn from the simulations.

A hardware-friendly algorithm for compressing hyperspectral images

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor, where the available power, time, and computational resources are limited. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompressed image for the ulterior hyperspectral imaging applications. The HyperLCA compressor aims to fulfill these requirements, providing an efficient lossy compression process that allows achieving very high compression ratios while preserving the most relevant information for the subsequent hyperspectral applications. One extra advantage of the HyperLCA compressor is that it allows to fix the compression ratio to be achieved. In this work, the effect of the specified compression ratio in the computational burden of the compressor has been evaluated, also considering the rest of the input parameters and configurations of the HyperLCA compressor. The obtained results verify that the computational cost of the HyperLCA compressor decreases for higher compression ratios, with independence of the specified configuration. Additionally, the obtained results also suggest that this compressor could produce real-time compression results for on-board applications.